Aminomethanesulfonic acid illuminates the boundary between full and partial agonists of the pentameric glycine receptor

  1. Josip Ivica
  2. Hongtao Zhu
  3. Remigijus Lape
  4. Eric Gouaux  Is a corresponding author
  5. Lucia G Sivilotti  Is a corresponding author
  1. Department of Neuroscience, Physiology and Pharmacology, Division of Biosciences, University College London, United Kingdom
  2. Vollum Institute, Oregon Health and Science University, United States
  3. Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, China
  4. Howard Hughes Medical Institute, Oregon Health & Science University, United States

Abstract

To clarify the determinants of agonist efficacy in pentameric ligand-gated ion channels, we examined a new compound, aminomethanesulfonic acid (AMS), a molecule intermediate in structure between glycine and taurine. Despite wide availability, to date there are no reports of AMS action on glycine receptors, perhaps because AMS is unstable at physiological pH. Here, we show that at pH 5, AMS is an efficacious agonist, eliciting in zebrafish α1 glycine receptors a maximum single-channel open probability of 0.85, much greater than that of β-alanine (0.54) or taurine (0.12), and second only to that of glycine itself (0.96). Thermodynamic cycle analysis of the efficacy of these closely related agonists shows supra-additive interaction between changes in the length of the agonist molecule and the size of the anionic moiety. Single particle cryo-electron microscopy structures of AMS-bound glycine receptors show that the AMS-bound agonist pocket is as compact as with glycine, and three-dimensional classification demonstrates that the channel populates the open and the desensitized states, like glycine, but not the closed intermediate state associated with the weaker partial agonists, β-alanine and taurine. Because AMS is on the cusp between full and partial agonists, it provides a new tool to help us understand agonist action in the pentameric superfamily of ligand-gated ion channels.

Editor's evaluation

Ivica et al. provide both functional and structural characterization of a relatively unstudied glycine receptor agonist. Their work supports their prior conclusions regarding the function of full vs. partial agonists, and provides a new look at a ligand that is structurally in between a full and partial agonist. This manuscript will be of interest to both biophysical and pharmacological investigations of ligand-gated ion channels.

https://doi.org/10.7554/eLife.79148.sa0

Introduction

The glycine receptor (GlyR), a member of the pentameric ligand-gated ion channel superfamily, is an anion-permeable channel that mediates fast synaptic inhibition in caudal areas of the central nervous system, particularly in the spinal cord. Studies of the GlyR have been instrumental in illuminating structure-function relationships and the molecular activation mechanism in the pentameric superfamily of receptors, because GlyR is well-suited both to single-channel recording to quantify detailed activation mechanisms (Burzomato et al., 2004; Lape et al., 2008) and to high-resolution structural investigations (Du et al., 2015).

We have recently shown that the degree of contraction induced by the agonist binding in the orthosteric neurotransmitter site is important in determining the efficacy with which agonists open this channel (Yu et al., 2021). Thus, the smallest agonist, the natural transmitter glycine, is the most efficacious agonist and when the channel is fully occupied by glycine, only open and desensitized structures can be detected. In single-channel recordings, a glycine-bound channel is either desensitized or open for more than 95% of the time and we shall refer to glycine as a full agonist. Larger compounds are weaker as agonists (e.g., partial agonists), and in their presence a third structural state is seen, an intermediate state where the binding site has closed on the agonist, but the pore is still in the resting closed conformation. A particularly well-characterized partial agonist is taurine, which produces approximately half of the maximum open probability response seen with glycine. Taurine (Figure 1A) has a bulkier anionic moiety than glycine (sulfonate instead of carboxylate) and has one additional methylene group separating the amino and anionic entities, respectively. As we inspected the structure of agonist-occupied GlyR binding sites, we wondered which of these two features was most important for determining agonist efficacy. We already knew that β-alanine, the carboxylate homolog of taurine, is less efficacious than glycine, but more efficacious than taurine. This strongly suggests that a structure intermediate between glycine and taurine, aminomethane sulfonic acid (AMS), should also be an agonist, and probably more efficacious than taurine. Surprisingly, we found only one study in the literature with data on AMS. In 1973, (Young and Snyder, 1973) reported that AMS displaced the binding of radioactive strychnine, a competitive antagonist of GlyR, but had no agonist effects when applied by iontophoresis onto native GlyRs in spinal cord neurons.

AMS is a highly efficacious agonist on zebrafish α1 GlyR.

(A) Structures of glycine, β-alanine, AMS, and taurine. (B) Whole-cell current responses of HEK293 cells elicited by application of agonist solutions (pH 5) with a U-tube. Cells were held at –40 mV. (C) Average concentration-response curves for glycine (black), β-alanine (green), AMS (blue), and taurine (red), n=6–9 cells. Responses of AMS, β-alanine, and taurine are normalized to those to a saturating concentration of glycine (100 mM) in each cell. AMS, aminomethanesulfonic acid.

Figure 1—source data 1

Data for the pooled dose-response curves in the figure.

https://cdn.elifesciences.org/articles/79148/elife-79148-fig1-data1-v2.xlsx

Here, we show that AMS is an efficacious GlyR agonist, whose activity was not detected in the past because it is chemically unstable at physiological pH and must be kept at acidic pH when employed in experimental studies. At acidic pH, GlyR gating is diminished, and taurine becomes a weaker agonist, whereas the maximum open probability elicited by glycine remains very high. Under these conditions, AMS is a strong agonist, more efficacious than β-alanine and almost as efficacious as glycine. Single particle cryo-electron microscopy (EM) structures of the AMS-bound GlyRs have features similar to those we reported for the GlyR bound to the most efficacious of the agonists, glycine, in that they populate only the open and the desensitized states, and have a compact agonist binding pocket. While the instability of AMS at neutral pH limits its general usefulness as an agonist, its efficacy provides a new tool to test hypotheses for the structural correlates of agonist action in pentameric ligand-gated channels.

Results

In the initial experiments, we dissolved AMS in a pH 7.4 solution and tested by whole-cell recording its effect at 100 mM on α1 GlyRs expressed in HEK 293 cells. Currents elicited by AMS were found to be inconsistent in amplitude over time. We also found that the pH of AMS solutions was unstable, drifting in a matter of few minutes. We hypothesized that this drift reflected AMS instability and decomposition at neutral pH. We then tried dissolving the compound in acidic solutions and found that at pH 5, 100 mM AMS solutions were stable, and remained within 0.1 of a unit of the initial pH for almost an hour, enough time to test their effect on GlyRs.

AMS is a highly efficacious GlyR agonist at acidic pH

Figure 1B shows whole-cell recordings of the responses of zebrafish α1 GlyR expressed in HEK 293 cells to U-tube applications of glycine, β-alanine, AMS, or taurine at pH 5. In order to maintain stable recordings, the cells were kept at physiological extracellular pH and only the agonist solutions were at pH 5. AMS had a strong agonist effect and evoked currents similar in amplitude and time course to those produced by glycine. Normalizing AMS responses against the maximum response to glycine in the same cell showed that AMS was almost as efficacious as glycine (89%; Figure 1C, Table 1). The other agonists, β-alanine and taurine, were clearly partial agonists, eliciting 69% and 18% of the maximum glycine response, respectively (Figure 1C, Table 1). Glycine is the most efficacious agonist known for this channel and was also the most potent agonist, with an EC50 of 0.98 mM, followed by β-alanine (4.5 mM), whereas taurine and AMS had similar low potencies (7.9 and 8.7 mM, respectively; Table 1).

Table 1
Whole-cell parameters for the action of agonists on the zebrafish α1 GlyR at pH 5.
Imax, nAEC50, µMnHIagonist/IGlymaxn
Glycine4.3±1.3980±3601.20±0.2118
AMS5.8±1.88700±31001.95±0.220.89±0.069
β-alanine3.5±0.34500±26001.20±0.400.69±0.096
Taurine1.1±0.57900±28000.85±0.150.18±0.086

We have recently shown (Ivica et al., 2022) that even modest extracellular acidification (pH 6.4) reduces both the potency and the efficacy of agonists on GlyR. This is confirmed at pH 5, where glycine, β-alanine, and taurine have lower potency than at physiological pH (Table 1). The EC50 values of glycine, β-alanine, and taurine increased from 0.19, 0.3, and 1.08 mM, their values at pH 7.4, to 0.98, 4.5, and 7.87 mM, respectively, at pH 5. At acidic pH, there was also a decrease in the maximum responses of β-alanine and taurine, to 69% and 18% of the maximum glycine responses (cf. 84 and 40% at physiological pH, respectively).

Single-channel recordings

The whole-cell recordings showed that AMS is almost as efficacious as glycine on GlyR, but whole-cell data cannot give an absolute measurement of agonist efficacy. Given the impairment in gating at acidic pH, we do not know how efficacious glycine is at pH 5.

We therefore measured the single-channel maximum open probability (Popen) elicited by high concentrations of the four agonists at pH 5.

Figure 2A shows continuous cell-attached recordings in the presence of 100 mM AMS in the pipette at pH 5. ‘Clusters’ of openings are separated by long closed intervals that are the expression of desensitization. The high Popen of the AMS clusters confirms that this compound is a highly efficacious agonist on GlyR. The similar traces below (Figure 2B) show that glycine is still very efficacious at pH 5. Despite the acidic pH, glycine clusters have a high Popen, somewhat higher than the AMS clusters. This impression is confirmed by the boxplots of Figure 2C, where the Popen values of 146 AMS clusters (from 11 patches) have a mean of 0.85, close to the 0.96 value of glycine (Table 2; p<<0.001, two-tailed randomization test).

Maximum open probability evoked by different GlyR agonists.

(A, B) Representative single-channel current recordings of zebrafish α1 GlyR activity evoked by high concentrations of agonists. Recordings were made in the cell attached configuration at +100 mV holding potential. (C) Boxplots of maximum Popen values for clusters with the different agonists (one point per cluster). Boxes and whiskers show the 25th and 75th and the 5th and 95th percentiles, respectively. The horizontal black line in the box is the median.

Table 2
Single-channel parameters of responses elicited by four agonists on zebrafish α1 GlyR.

maxPopen was measured from n clusters of activation and reported as mean ± SD. Data at pH 7.4 are from Ivica et al., 2021.

pHGlycineβ-alanineAMSTaurine
5maxPopen0.96±0.060.54±0.240.85±0.190.12±0.12
median Popen0.9760.5660.9310.060
npatches(nclusters)8 (92)7 (52)11 (146)9 (37)
Agonist concentration (mM)100100100500
7.4maxPopen0.97±0.050.91±0.21/0.66±0.24
median Popen0.9890.978/0.728
npatches(nclusters)10 (48)7 (30)/7 (71)
Agonist concentration (mM)1030100

The gating inhibition of GlyR at acidic pH was clear for the other two agonists. The compound β-alanine is an efficacious agonist on zebrafish α1 GlyR at pH 7.4 (maximum Popen=0.91; Ivica et al., 2021), but its maximum Popen is only 0.54 at pH 5 (Figure 2B and C), where it is clearly less efficacious than glycine and AMS.

Among the four agonists, taurine was the least efficacious. The mean maximum Popen measured from clusters of activation evoked by 500 mM taurine was 0.12±0.12, a value five times smaller than at pH 7.4 (0.66; Ivica et al., 2021).

Cryo-EM structure determination of AMS-bound GlyR

We used styrene maleic acid (SMA) polymer to extract recombinant, zebrafish α1 GlyRs, together with endogenous lipids (Figure 3—figure supplement 1), because our earlier work showed preservation of physiological receptor states with this reagent (Yu et al., 2021). We found that while AMS triggered the aggregation of GlyRs, we could minimize receptor self-association by employing continuous thin carbon film as a support. Because of the instability of AMS at neutral pH (see Discussion), the GlyR cryo-EM grids were flash-frozen in less than 1 min after adding the ligand (see Materials and methods for details).

The single-channel data show that AMS is highly efficacious and produced clusters of openings (Figure 2A) that lack the long shut states seen with partial agonists and have a high maximum Popen, approaching that of glycine. We therefore hypothesized that AMS-bound GlyR should populate only open and desensitized states. In agreement with our hypothesis, the single particle cryo-EM analysis revealed open, desensitized and expanded-open states (Figure 3, Supplementary file 1, Figure 3—figure supplement 2), similar to our findings with glycine (Yu et al., 2021). Like with glycine, with AMS we did not capture the agonist-bound (intermediate) closed state seen with the partial agonists taurine and GABA (Yu et al., 2021). The overall resolutions for open, desensitized, and expanded-open states was 2.8 Å, 2.9 Å, and 3.1 Å, respectively. Importantly, these reconstructions have well-resolved extracellular domain (ECD) and transmembrane domain (TMD) densities, allowing us to observe conformational differences (Figure 3—figure supplement 3, Supplementary file 1) in the structures.

Figure 3 with 3 supplements see all
Cryo-EM analysis of structures of zebrafish α1 GlyR bound to AMS.

(A–C) Cryo-EM density maps for desensitized, open, and expanded-open states with one subunit highlighted. The AMS density is in red. (D–F) Atomic models for desensitized, open, and expanded-open states. Shown are GlyR in cartoon representation, AMS in sphere representation (red), and N-glycans in stick representation. AMS, aminomethanesulfonic acid; EM, electron microscopy.

The pore domains of AMS-bound states adopt conformations similar to those we reported for glycine-bound and taurine-bound states. The tilted conformation of the M2 helices in both the desensitized and open state creates a constriction of the pore at the Pro residue in the –2′ position (Figure 4A–B). For the AMS-bound desensitized state, the diameter of this constriction is 3.2 Å, too narrow to allow the permeation of partially hydrated chloride ions (Bormann et al., 1987; Hille, 2001), confirming this state is non-conducting (Figure 4A). For the AMS-bound open state, the constriction of the pore is 5.4 Å in diameter (Figure 4B), indicative of a conducting state (Yu et al., 2021). The pore radius plots illustrate the similarities between the structures of the receptor bound to different agonists in the open and desensitized states (Figure 4C–D).

Comparison of ion channel pores.

(A, B) Shape and ion permeation pathway for AMS-bound desensitized (see also (C)) and open (see also (D)) states. M2 helices and key amino acids are shown in ribbon and stick representation, respectively. Purple, green, and red spheres define radii >3.5 Å, 1.8–3.5 Å, and <1.8 Å. (C, D) Profiles of pore radii calculated by the HOLE program for desensitized (A) and open (B) states bound with AMS, taurine, and glycine. The Cα position of R268 was set to 0. AMS, aminomethanesulfonic acid.

The neurotransmitter binding site is the first element of the receptor to productively interact with agonists and examining conformational changes in this area helps us understand agonist-induced channel activation. Guided by the AMS density (Figure 5—figure supplement 1A), AMS can be unambiguously placed in the binding pockets. In both the desensitized and open states, the densities contributed by AMS are well resolved, with the larger sulfonate group at the entrance of the binding pockets (Figure 5—figure supplement 1A). All agonist binding sites appear fully occupied by AMS molecules, confirming that the compound had not degraded in our experimental conditions. Like with the full agonist glycine (Du et al., 2015; Yu et al., 2021), the amino group of AMS is sandwiched by residues on the (+) subunit, loop B F175 and loop C F223, with distances compatible with a cation-π interaction. These residues are important in GlyR agonist recognition (Schmieden et al., 1993) and form part of the canonical aromatic box of the binding site (Pless et al., 2008, reviewed in Lynagh and Pless, 2014). We observed also a potential hydrogen bond between the main chain carbonyl of loop B F175 and the amino group of AMS. At the other end of the agonist molecule, the sulfonate group participates in multiple hydrogen bonds with amino acids that include R81 and S145 derived from β strands 2 and 6 on the (−) subunit, respectively, and T220 in loop C of the (+) subunit (Figure 5A–B), all residues that are important for the agonist activation of GlyRs (Grudzinska et al., 2005; Yu et al., 2014; Vandenberg et al., 1992). The interactions we observed for AMS are similar to those of glycine (Figure 5B–C), underscoring the functional similarity between these two efficacious compounds.

Figure 5 with 1 supplement see all
Comparison of agonist binding sites.

(A) Two adjacent GlyR subunits are shown in cartoon representation. The agonist binding pocket is indicated by a black box. (B, C) Stereo figures of the binding sites showing likely hydrogen and cation-π interactions with AMS (B) and glycine (C) bound, respectively. Numbers are the distances in Å of probable cation- π interactions. Numbering of residues includes the signal peptide of 16 amino acids. (D) Comparison of the positions of key binding residues in the open states of the glycine (salmon), taurine (green), and AMS (blue) complexes, obtained by superposing the respective ECDs. (E) Schematic diagram illustrating the distances (Å) between the Cα atoms of key amino acids in glycine-, taurine-, and AMS-bound open states. AMS, aminomethanesulfonic acid; ECD, extracellular domain.

Interestingly, we found that the amino group of the partial agonist taurine forms a hydrogen bond with loop B S174 (Figure 5—figure supplement 1B), a (+) side interaction that is absent in AMS and glycine, because of their shorter length. We had previously noted a similar possible interaction between loop B S174 and the amino group of another partial agonist, GABA (Yu et al., 2021). Considering that the interactions with the (−) subunit are similar for the two groups of agonists, full and partial, it is tempting to speculate that the difference in the amino group interactions on the principal side may contribute to the difference in their efficacy.

Our previous structural studies showed that agonist efficacy is correlated with the degree of contraction of the binding pocket (Yu et al., 2021). Because AMS is nearly as efficacious as glycine (Figures 1 and 2), we expect it to produce a contraction of the binding pocket similar to that caused by glycine. The volumes of glycine, AMS, and taurine binding pockets are 130 Å3, 125 Å3, and 151 Å3, respectively, showing that the binding pocket, when bound with AMS or glycine, takes up a conformation that is more compact than that seen with the partial agonist taurine. By overlapping the binding pockets, we found that, in the glycine-bound site, the movement of loops B and C brings them closer to the ECD-TMD interface than in the AMS- and taurine-bound sites (Figure 5D–E, Figure 5—figure supplement 1C), which may be one reason for the higher efficacy of glycine.

The perturbation introduced by the agonist in the binding site spreads to the pore by eliciting conformational changes at the ECD-TMD interface, which can be detected when we compare the apo and open states (Yu et al., 2021). We scrutinized the conformational differences of the ECD-TMD in GlyR open states bound to different ligands. We found that the ECD-TMD interfaces are overall similar between glycine, AMS, and taurine (Figure 6A–B), as indicated by distances between the centers of mass of the secondary structure elements.

Comparison of the ECD-TMD interface in different agonist-bound complexes.

(A) Superposition of the ECD-TMD interface of the open states of the glycine (salmon), taurine (green), or AMS (blue) bound forms. The key amino acids at the ECD-TMD interface are shown in stick representation. Key secondary structure elements are labelled. The blue spheres represent the centers of mass of the secondary structure elements for the AMS-bound structure. (B) Schematic diagram illustrating the distances (Å) of the center of mass points shown in panel (A) of glycine-, taurine-, and AMS-bound open states. ECD, extracellular domain; TMD, transmembrane domain.

Discussion

Instability of AMS at physiological pH

Our initial findings, that AMS produced inconsistent responses, and that the pH of its solutions drifted relatively quickly from neutral pH, suggested chemical instability. Indeed, prior studies have shown that AMS is unstable and decomposes to formaldehyde and sulfur dioxide (Frankel and Moses, 1960; Moe et al., 1981). The mechanism of decomposition of AMS and other α amino sulfonic acids is thought to require the availability of the electron pair of the unprotonated amino group (Moe et al., 1981). This implies that AMS should be maximally stable when the amino group is protonated and the compound is a zwitterion. The amino group on AMS is not very basic, with a pKa value of 5.75 (cf. 9.06 and 9.78 for the amino groups of taurine and glycine, respectively; Benoit et al., 1988). At physiological pH, only 2% of AMS is expected to be in the stable zwitterion form. Full protonation requires pH values too acidic to be compatible with stable electrophysiological recordings. We opted to test AMS at a compromise pH of 5, where 85% of this compound is a zwitterion. In order to ensure cell health, we kept cells at neutral pH and switched them to pH 5 only during agonist application. In the structural studies, particular care was taken to freeze the grids as quickly as possible after mixing agonists and receptors and our analysis showed density features consistent with undegraded AMS molecules. We were rewarded in these experimental choices by our demonstration that AMS is an efficacious agonist at both functional and structural levels.

AMS behaves structurally as a highly efficacious agonist

Our whole-cell and single-channel recordings demonstrated AMS is an efficacious agonist, which produces a mean maximum Popen of 0.85 (cf. 0.96 for glycine) and is clearly more efficacious than β-alanine, which until now was considered the second most efficacious agonist of GlyRs (Ivica et al., 2021).

Cryo-EM structural analysis of AMS-bound GlyRs detected all the structural features that we have associated with the high efficacy of glycine. The partial agonist-bound closed state (Yu et al., 2021) is absent from both the AMS-bound and glycine-bound GlyRs. Indeed, our data show that, for the AMS-bound receptor, 94.3% and 3.4% of the particles are in open and desensitized state classes, respectively (Supplementary file 1), consistent with the notion that the particle fractions for desensitized states are much smaller than open states when the receptor is isolated via SMA, compared to when it is incorporated into nanodiscs (Yu et al., 2021). Further confirmation comes from inspecting the agonist binding site. The most obvious feature is that the binding pocket is as compact when bound to AMS as with glycine (Yu et al., 2021). The measurements for the glycine-bound and AMS-bound pockets are almost identical, and both are more compact than those obtained with the partial agonist taurine. Interestingly, the potency of AMS is low, as its EC50 is about tenfold higher than that of glycine, and similar to the EC50 of taurine. Thus, even though AMS has an additional oxygen for the formation of another hydrogen bond, it must bind with much lower affinity than glycine, underscoring the lack of correlation between efficacy and affinity of these agonists.

Both AMS and glycine form multiple interactions with the (+) and (−) subunits (Figure 5B). These interactions are similar but not identical for partial and full agonists, and we were able to observe some differences. For instance, the interaction between loop B S174 and the agonist amino group, which was observed for partial agonist taurine (Figure 5—figure supplement 1B) and GABA (Yu et al., 2021), was not seen with either AMS and glycine (Figure 5A–B) probably because these molecules are shorter. It is tempting to speculate that this additional interaction may limit the movement of loop B, a domain whose conformation has a close relationship with the ECD-TMD interface. This limitation in movement may contribute to the low efficacy of taurine and GABA.

Glycine has uniquely high efficacy

The natural agonist of GlyRs, the neurotransmitter glycine, remains the most efficacious agonist. It has generally been thought that this is simply due to glycine being the smallest molecule, but our discovery of the agonist effects of AMS allowed us to consider this question in greater detail, examining the effects of making the agonist longer or changing its anionic moiety from a carboxylate to a larger sulfonate group. In our functional work, we characterized a panel of four agonists, chosen on the basis of their related structures. Figure 7 shows how swapping the carboxylate group with sulfonate (vertical arrows) in glycine and β-alanine produces AMS and taurine, respectively, whereas lengthening the carbon chain by one methylene (horizontal arrows) in glycine and AMS produces β-alanine and taurine, respectively.

Thermodynamic cycle for the four GlyR agonists functionally characterized, showing their structure and example sweeps of the single-channel activity they elicit.

Figure 7 shows how both changes in structure decrease agonist efficacy. Of the two structure modifications we tested, lengthening the distance between the two charges on these amino acids causes the greater decrease in efficacy. The example clusters in Figure 7 show that the median maximum open probability decreases from 0.98 to 0.57 (glycine to β-alanine, corresponding to a decrease in Eeff from 40.7 to 1.30) and from 0.93 to 0.06 (AMS to taurine, corresponding to a decrease in Eeff from 13.5 to 0.06). It is worth noting that further lengthening β-alanine by one methylene produces GABA, a weak partial agonist.

Increasing the size of the negatively charged group from carboxylate to sulfonate had a modest effect for the shorter agonists, and the overall efficacy of glycine was decreased by less than twofold with the change to AMS. The effect of this swap is greater on the longer agonists. This can better be examined by thermodynamic cycle analysis of the free energy changes associated with gating for the four related agonists (Figure 7). These values can be obtained from our estimates of overall efficacy yielded by single-channel measurements of maximum open probability. In the thermodynamic cycle analysis of the free energy values that underlie gating, we found that there is marked coupling between the effects of the two changes in agonist structure, with an estimated coupling energy of 1.03±0.2 kcal/mol (Figure 7). This means that the combined effect is more than we would anticipate by the linear sum of the effects of each change. In other words, taurine is less efficacious than we would predict. Conversely, glycine is more efficacious than we would expect it to be, given that it is a shorter taurine, where the sulfonate is replaced by a carboxylate.

Our previous work has shown that the much greater efficacy of glycine compared with taurine or GABA is associated with a tighter conformation of the binding pocket bound to glycine. Our new data show that AMS is almost as full an agonist as glycine and that the binding pocket bound to AMS is as compact as with glycine, at the resolution of our data. However, the structural correlates for the small difference in efficacy between AMS and glycine have so far proven elusive and may require further structural studies, at higher resolution.

In conclusion, our electrophysiological and cryo-EM experiments demonstrate that AMS is an agonist with high efficacy, greater than that of β-alanine and second only to that of glycine. The availability of AMS as a new tool is particularly useful, because structural changes with activation are relatively small in channels of this superfamily, and the differences between agonists of different efficacy are subtle. Our present work with AMS allowed us to confirm our previous observations with glycine on the structural correlates of agonist efficacy in GlyRs and also to propose new structural features that may be important in agonist activation of channels in this superfamily.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Cell line (Homo sapiens)HEK293AInvitrogen: https://www.thermofisher.com/order/catalog/product/R70507?SID=srch-hj-R705-07Catalog no. R705-07lot #1942007; certified mycoplasma free by supplier
Cell line (Spodoptera frugiperda)Sf9Thermo Fisher Scientific: https://www.thermofisher.com/order/catalog/product/12659017Catalog no. 12659017lot 421973 mycoplasma tested at OHSU
Recombinant DNA reagentZebrafish GlyRa1 subcloned in pcDNA3 vectorfhttps://doi.org/10.1016/j.cell.2021.01.026Uniprot identifier: O93430
Recombinant DNA reagentZebrafish GlyRa1 subcloned in pFastBac Dual vectorThis paperUniprot identifier: O93430
Software, algorithmOriginPro softwarehttps://www.originlab.com/Origin 2019b
SoftwareClampex software, Molecular Deviceshttps://support.moleculardevices.com/s/article/Axon-pCLAMP-10-Electrophysiology-Data-Acquisition-Analysis-Software-Download-PageClampex 10.7
SoftwareDC-STATSPlested and Lape, 2020Our lab; https://github.com/DCPROGS/DCSTATS/releases/tag/v.0.3.1-alpha
SoftwareCVFITLape, 2020bhttps://github.com/DCPROGS/CVFIT/releases/tag/v1.0.0-alpha
SoftwareMotionCor2https://doi.org/10.1038/nmeth.4193
RRID:SCR_016499http://msg.ucsf.edu/em/software/motioncor2.html
SoftwarecryoSparchttps://doi.org/10.1038/nmeth.4169
RRID:SCR_016501https://cryosparc.com/
SoftwareCoothttps://doi.org/10.1107/S0907444910007493

RRID:SCR_014222https://www2.mrc-lmb.cam.ac.uk/personal/pemsley/coot/
SoftwarePhenixhttps://doi.org/10.1107/S2059798318006551
RRID:SCR_014224https://www.phenix-online.org/
SoftwarePymolPyMOL Molecular Graphics System, Schrodinger, LLCRRID:SCR_000305http://www.pymol.org/
SoftwareUCSF ChimeraXhttps://doi.org/10.1002/pro.3235
RRID:SCR_015872http://cgl.ucsf.edu/chimerax/
Antibodyanti-human CD235a-APC (mouse monoclonal)Thermo Fisher ScientificCat#: 17-9987-42; RRID:AB_2043823FACS (5 µl per test)
Recombinant DNA reagentPLKO-GFP (plasmid)This paperGFP version of pLKO.1-Puro
Recombinant DNA reagentPLKO.1-Puro (plasmid)Sigma-AldrichRRID:Addgene_10878Pol III based shRNA backbone
Sequence-based reagentGipc1_FThis paperPCR primersGGGAAAGGACAAAAGGAACCC
Sequence-based reagentGipc1_RThis paperPCR primersCAGGGCATTTGCACCCCATGCC
Sequence-based reagentsiRNA: nontargetin controlThermo Fisher Scientific4390843Silencer Select
Peptide, recombinant proteinStreptavidinThermo Fisher ScientificCat#: 434302
Commercial assay or kitIn-Fusion HD CloningClontechClontech: 639647
Chemical compound, drugCBR-5884Sigma-AldrichSML1656
Chemical compound, drugSL30010 (SMALP 30010P)Polyscopehttp://polyscope.eu/markets/polyscience/

Cell culture

Request a detailed protocol

HEK293A (from Life Technologies/Invitrogen, now Thermo Fisher Scientific; certified mycoplasma free by supplier) were maintained in an incubator at 37°C, 5% CO2, and 95% humidity. The culture medium was Dulbecco’s modified Eagle’s medium (Gibco, 41966029) supplemented with 10% v/v heat-inactivated fetal bovine serum (Gibco), 100 units/ml penicillin G, and 100 µg/ml streptomycin sulfate (Gibco). Cell aliquots were frozen from the third passaging since the purchase batch. For experiments, an aliquot was thawed and then passaged up to 25 times, every 2–3 days after reaching 70–80% confluence.

For transfection, cells were plated on 13 mm poly-L-lysine glass coverslips, placed inside 35 mm cell culture dishes containing 2 ml growth medium. The transfection was performed with the calcium phosphate precipitation method (Groot-Kormelink et al., 2002). The total amount of DNA per coverslip was 3 µg. The DNA mixture contained 2% plasmid coding for zebrafish α1 GlyR (Uniprot accession number O93430), 20% plasmid coding for the enhanced green fluorescence protein (eGFP) and 78% of ‘empty’ pcDNA3 plasmid. The empty plasmid was introduced to optimize the level of receptor expression and the eGFP to identify the transfected cells. After 4–8 hr, the transfection medium was replaced with fresh growth medium. Electrophysiological recordings were performed 24–48 hr after transfection.

Whole-cell recordings

Request a detailed protocol

Recording pipettes were pulled with a P-97 horizontal puller (Sutter Instruments), using thick-walled borosilicate capillaries (GC150F-7.5; Harvard Apparatus, UK). The tips were fire-polished with a microforger (Narishige, Japan) to a resistance of 3–5 MΩ. Currents were recorded with an Axopatch 200B amplifier (Molecular Devices), prefiltered with the amplifier’s built in 5 kHz low-pass Bessel filter and sampled at 20 kHz with Digidata 1550B digitizer (Molecular Devices) to a computer hard drive with Clampex 10.7 software (Molecular Devices). Currents were recorded at a nominal –40 mV holding potential (–50 mV if corrected for liquid junction potential). Access resistance was never higher than 8 MΩ and was compensated by 60–80%, with a maximum voltage error of 10 mV. For the figures, current traces were filtered with an additional 1 kHz low pass Gaussian filter in Clampfit 10.7.

The bath extracellular solution contained (in mM): 112.7 NaCl, 20 sodium gluconate, 2 KCl, 2 CaCl2, 1.2 MgCl2, 10 tetraethylammonium Cl (TEACl), 30 glucose, and 10 HEPES; the pH was adjusted with NaOH to 7.4.

Agonist solutions were freshly prepared in pH 5 extracellular solution. For this solution, the 10 mM HEPES buffer was replaced with 6.7 mM sodium acetate and 3.3 mM acetic acid.

The intracellular solution contained (in mM): 101.1 potassium gluconate, 11 EGTA, 6 KCl, 1 CaCl2, 1 MgCl2, 20 TEACl, 2 MgATP, 10 HEPES, and 40 sucrose; the pH was adjusted to 7.2 with KOH.

Agonists were applied to a cell with a custom-made U-tube. The speed of agonist application was determined by positioning the recording pipette just above the cell and measuring the 20–80% rise time of the current evoked by U-tube application of diluted bath solution (50%) to the recording pipette. Rise time was typically 2 ms (the U-tube tool was discarded if rise time was slower than 20 ms).

Agonist was applied every 30 s including application of a saturating concentration of 100 mM glycine every 3–4 applications to monitor current rundown. If the rundown was more than 30%, the cell was discarded from analysis. A full dose-response curve was obtained in each cell, and the responses measured with Clampfit 10.7 were fitted with the Hill equation with custom-made software (Lape, 2020a). The parameters of the dose response fits obtained from n cells (EC50, Imax, and nH) are reported as mean ± SD. For the figures, the dose-response curves were normalized to the maximum response in each cell, pooled together and refitted with the Hill equation.

Single-channel recordings

Request a detailed protocol

Glass pipettes were fabricated with a Sutter P-97 horizontal puller from thick-walled filamented borosilicate glass (GC150F-7.5; Harvard Apparatus, UK) and fire-polished just before use with a microforge to a resistance of 8–12 MΩ. In order to minimize electrical noise, pipette tips were coated with Sylgard 184 (Dow Corning, Dow Silicones, UK). Cell-attached single-channel currents were recorded with an Axopatch 200B in patch configuration with a gain set to 500. The built-in low pass Bessel filter was set to 10 kHz and holding voltage was +100 mV. Single-channel currents were sampled at 100 kHz with a Digidata 1440 digitizer. For analysis, currents were additionally filtered with a 3 kHz low pass Gaussian filter and resampled to 33.3 kHz with Clampfit 10.7 software. In the recording pipette, agonists were dissolved in the pH 5 extracellular solution. Openings of GlyR in the presence of high agonist concentrations appear as clusters of openings, namely stretches of high open probability activity separated by long desensitized closed times that are not concentration dependent. We measured open probability with Clampfit 10.7 in clusters that had no double openings and were at least 100 ms long. Openings and closing events within the cluster were idealized with a threshold crossing method and cluster open probability was obtained as the ratio between the total open time and the duration of the cluster. Boxplots showing Popen values were created with OriginPro 2019 (OriginLab).

Nonparametric randomization test (two-tail, non-paired; 50,000 iterations) was used to determine p values for the difference between cluster open probabilities being greater than or equal to the observed difference (DC-Stats software: Plested and Lape, 2020).

Thermodynamic cycle analysis

Request a detailed protocol

For each agonist, the maximum open probability reached, maxPopen, is linked to the equilibrium constant for gating by a simple relation.

(1) maxPopen= Eeff Eeff +1

where Eeff is the overall gating constant (e.g., incorporating the flipping/priming and the opening steps; Burzomato et al., 2004). We used Equation 1 to estimate Eeff from the maxPopen values measured from clusters, that is, obtaining one Eeff value per cluster.

The structures of four agonists used in this work were closely related and could be arranged in the cycle shown in Figure 7. Swapping the carboxylate groups of glycine and β-alanine with sulfonate gives AMS and taurine, respectively. Similarly, lengthening the carbon chain of glycine and AMS with one methylene group gives β-alanine and taurine, respectively.

The existence of these structural relations between the agonists and our ability to estimate for each agonist a gating constant allowed us to carry out a thermodynamic cycle analysis (Carter et al., 1984; Lee and Sine, 2005) in order to assess whether the effect of combining the two structural modifications was predictable from the effects of each of the two modifications alone. For each structural modification of the agonist, the free energy change is given by:

(2) G=-RTln (Eeff m Eeff)

where Eeff m is the gating constant for the modified agonist, R is the gas constant (1.987 cal K–1 mol–1), and T is the absolute temperature (295K). The free energy change is the same along both pathways (see Figure 7).

(3) ΔG1+ΔG2=ΔG2+ΔG1

The coupling free energy ΔΔGint of a thermodynamic cycle can be obtained as the difference between ΔG1 − ΔG1′ or ΔG2 – ΔG2′. If the ΔΔGint is equal to 0, there is no interaction between the two structural changes, and their combined effect equals the sum of the individual effects.

We calculated the standard deviation of the mean for the free energy change and the free energy of coupling (ΔΔGint = ΔG1 − ΔG1′) from their distributions obtained by bootstrapping (10,000 runs with replacement) the Eeff values from each agonist.

Protein expression and purification

Request a detailed protocol

cDNA encoding the zebrafish full-length GlyR α1 (NP_571477) was cloned into the vector pFastBac1 for baculovirus expression in Sf9 cells (Thermo Fisher Scientific). Cells were tested routinely to check they were free from mycoplasma by DNA fluorochrome staining (CELLshipper Mycoplasma Detection Kit M-100 from Bionique).

A thrombin site and an 8× His tag were added at the C-terminus. Sf9 cells were cultured in SF9-900 III SFM at 27°C. To express GlyR, Sf9 cells, at the density of 2–3 million/ml, were infected by the baculovirus followed by incubation at 27°C for 72 hr.

Cells were harvested by a J20 centrifuge at 5000×g for 20 min. The cell pellet was suspended in 200 ml ice-cold buffer made of 20 mM Tris pH 8.0 and 150 mM NaCl (TBS), supplemented with 0.8 μM aprotinin, 2 µg/ml Leupeptin, 2 mM pepstain A, and 1 mM phenylmethylsulfonyl fluoride (PMSF). The cells were then disrupted by sonication. The large debris was removed by centrifugation at 10,000×g for 10 min. Membrane was pelleted by a Ti45 rotor at 45k rpm for 1 hr, resuspended in ice-cold TBS and homogenized by a Dounce homogenizer. The membrane was then mixed with a final concentration of 1% SMA copolymer XIRAN 30010 and incubated in a cold room for 2 hr. After centrifugation at 45k rpm for 1 hr using a Ti45 rotor, the supernatant was then incubated with 10 ml pre-equilibrated Ni-NTA beads for 6 hr. The Ni-NTA beads were loaded to an XK-16 column and washed by 10-column TBS containing 35 mM imidazole. The GlyR was eluted by 250 mM imidazole. Size exclusion chromatography (SEC) was then performed to further purify the protein.

Cryo-EM sample preparation and data collection

Request a detailed protocol

The GlyR peak fraction eluted from SEC was diluted to 80 µg/ml. Considering the instability of AMS, a 100 mM AMS solution was prepared in TBS immediately before freezing the grids. Equal volumes of GlyR prep and 100 mM AMS were mixed, resulting in a final receptor concentration of 40 µg/ml. Dissolving AMS in TBS to a concentration of 100 mM produced a solution with pH 4.6. This rose to 4.95 when mixed with an equal volume of TBS (in order to estimate the final pH of the sample). At this pH, we expect 85% of AMS to be in zwitterion form.

Application of samples to glow-discharged Quantifoil 2/2 grids covered by 2 nm continuous carbon grids (3.5 µl per grid) was followed by flash-freezing in liquid ethane cooled by liquid nitrogen, using an FEI Mark IV cryo-plunge instrument.

The data set was collected on a Titan Krios operated at 300 kV, equipped with a BioQuantum K3 camera, using CDS mode at the magnification of 105,000×, corresponding to super-resolution pixel size of 0.4155 Å. The defocus range was set from –1.2 to –2.2 μm. Each micrograph was recorded with a dose rate of 8 e2/s, resulting a total dose of 34.4 e2.

Image processing

Request a detailed protocol

The motion correction, CTF estimation, and particle picking were performed by cryoSparc (Punjani et al., 2017). The desensitized state glycine-bound GlyR in SMA (EMD-20388) was used as the initial model. After the extraction of particles, two rounds of 3D classification were performed by cryoSparc to remove the junk particles. One round of heterogeneous refinement with six classes was performed. Two classes with GlyR features containing 449,976 particles were selected. A second round of the heterogeneous refinement with four classes was performed and one class with good TMD features containing 283,117 particles was chosen. This was followed by a round of non-uniform refinement performed in cryoSparc. These good particles were then exported to Relion 3.1 (Zivanov et al., 2018) for one round of 3D classification. During the 3D classification in Relion 3.1, the particle alignment was closed and the data set was separated into eight classes with the T value of 20. The 3D classes with good TMD are selected for the subsequent 3D reconstruction. The final maps were generated by a direct reconstruction using Relion (Zivanov et al., 2018). The final FSC curves and local resolution maps were estimated by cryoSparc (Punjani et al., 2017). All maps are sharpened by LocScale (Jakobi et al., 2017).

Model building

Request a detailed protocol

The model building commenced with the replacement of the ligand derived from the prior GlyR structures bound with taurine. The initial models for AMS-bound open, desensitized, and expanded-open states were taurine-bound open, desensitized, and expanded-open, respectively (Yu et al., 2021). The procedure to build each of the three AMS-bound states was the same. Take AMS-bound open state for example: the taurine-bound open state was first rigid-body fitted to the corresponding map using UCSF Chimera software (Pettersen et al., 2004). Coot (Emsley and Cowtan, 2004) was used to replace the ligand taurine with AMS. The structure was then manually adjusted in Coot followed by a round of Phenix (Afonine et al., 2018) refinement. The map to model cross-correlation values between the final model and map was 0.78, 0.84, and 0.81 for open, desensitized, and expanded-open states, respectively.

Note that our numbering of residues of the zebrafish GlyR α1 subunit includes the signal peptide which is predicted to be 16 residues.

Data availability

The coordinates and volumes for the cryo-EM data have been deposited in the Electron Microscopy Data Bank under accession codes EMD-26316, EMD-26315, and EMD-26317. The coordinates have been deposited in the Protein Data Bank under accession codes 7U2N, 7U2M and 7U2O. All data generated during this study is included in the manuscript and supporting files. Source data spreadsheets are provided for the electrophysiology data.

The following data sets were generated
    1. Zhu H
    2. Gouaux E
    (2022) EMDataResource
    ID EMD-26315. A novel compound mimics the structural and functional effects of the full agonist glycine on glycine channels-desenstized state.
    1. Zhu H
    2. Gouaux E
    (2022) EMDataResource
    ID EMD-26316. A novel compound mimics the structural and functional effects of the full agonist glycine on glycine channels-open state.
    1. Zhu H
    2. Gouaux E
    (2022) EMDataResource
    ID EMD-26317. A novel compound mimics the structural and functional effects of the full agonist glycine on glycine channels-expanded open state.
    1. Zhu H
    2. Gouaux E
    (2022) RCSB Protein Data Bank
    ID 7U2M. A novel compound mimics the structural and functional effects of the full agonist glycine on glycine channels-desenstized state.
    1. Zhu H
    2. Gouaux E
    (2022) RCSB Protein Data Bank
    ID 7U2N. A novel compound mimics the structural and functional effects of the full agonist glycine on glycine channels-open state.
    1. Zhu H
    2. Gouaux E
    (2022) RCSB Protein Data Bank
    ID 7U2O. A novel compound mimics the structural and functional effects of the full agonist glycine on glycine channels-expanded open state.

References

    1. Emsley P
    2. Cowtan K
    (2004) Coot: model-building tools for molecular graphics
    Acta Crystallographica. Section D, Biological Crystallography 60:2126–2132.
    https://doi.org/10.1107/S0907444904019158
  1. Book
    1. Hille B
    (2001)
    Ionic Channels of Excitable Membranes
    Sinauer Associates.

Decision letter

  1. Marcel P Goldschen-Ohm
    Reviewing Editor; University of Texas at Austin, United States
  2. Kenton J Swartz
    Senior Editor; National Institute of Neurological Disorders and Stroke, National Institutes of Health, United States
  3. Marcel P Goldschen-Ohm
    Reviewer; University of Texas at Austin, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Aminomethanesulfonic acid illuminates the boundary between full and partial agonists of the pentameric glycine receptor" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, including Marcel P Goldschen-Ohm as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen Kenton Swartz as the Senior Editor.

The reviewers have discussed their reviews with one another; please address all of the reviewer comments below.

Reviewer #1 (Recommendations for the authors):

1. lines 234-240, 290-292 – The differences in the binding site structures between glycine, AMS, and taurine in Figure 5D, E appear to be incredibly small with respect to the resolution of the structural models. I am skeptical about all of the conclusions that the authors draw in this section about taurine not producing as compact a binding site as glycine or AMS and glycine shifting loops B and C closer to the TMD. Based on the model resolutions, wouldn't it be more apt to conclude that there are no obvious differences in binding site compactness, etc.? If the authors disagree, could you please offer some additional explanation as to why the very small differences (which the authors refer to as "subtle") should be considered sufficiently well resolved to be meaningful? Perhaps densities can be used to make an argument here? Also, the same question for Figure 6 ECD-TMD interface, regarding subtle differences in compactness of this interface with taurine vs glycine or AMS bound.

2. lines 177, 237, 251 – related to above, please define what "subtle" means.

3. The relative lack of a desensitized conformation in the SMA-extracted glycine receptor particles as compared to what would be expected based on functional data should be discussed. Part of this discussion could simply reference the comparison between nanodiscs and SMA in Yu et al., 2021, but it should be at least briefly discussed here as it is highly relevant to the interpretation of the structures.

4. In Figure 3—figure supplement 2, it is clear that the open, desensitized, and expanded-open clusters were selected based on their similarity to previously identified structures. However, the desensitized and expanded-open clusters account for only a couple percent of the total particles, whereas other clusters that were discarded account for larger fractions of the particles. Could the authors please provide a more detailed discussion of what criteria were used to discard these other clusters?

5. line 342 – extra "that".

Reviewer #2 (Recommendations for the authors):

1. The authors write (lines 60-62) that the binding of high efficacy agonists like glycine leads to channels adopting either open or desensitized states, while binding of partial agonists results in an additional third (intermediate) state that represents agonist-bound closed conformations of the channel. This isn't really true, however, or is an oversimplification that could be confusing to the reader. If a maximally-effective concentration of glycine has a Po of 0.96, there clearly are brief transitions to glycine-bound closed states of the channel. Wouldn't it be more accurate to state that as agonist efficacy increases, the duration of ligand-bound closed states decreases? Also, what about when low (non-saturating) concentrations of glycine are used? Would one still expect the two states the authors mention?

2. In Figure 4D, the AMS_Open and Taurine_Open lines look more similar to one another (except when the distance along the pore axis >30A) than they do with the Glycine_Open line. Also, the taurine and AMS open lines both look markedly different from the glycine line between 10 and 18 angstroms along the pore axis. What is the significance of this?

3. The single channel data obtained using 100 mM taurine assumes that this concentration is truly maximally-effective. Is this the case? If it is not, could that explain the statement on line 336 that "…taurine is less efficacious than we would predict."?

4. Line 320. How do max Po values of 0.96 and 0.54 yield Eeff values of 60 and 3.8 from the equation Po = Eeff/(1 + Eeff)? Shouldn't they be closer to 24 and 1.2? The same question applies to AMS and taurine on line 321.

5. Do the authors have any thoughts on why the Po, and thus efficacy, of β-alanine and especially taurine went up relative to glycine as the pH was raised from 5 to 7.4 (Table 2)?

https://doi.org/10.7554/eLife.79148.sa1

Author response

Reviewer #1 (Recommendations for the authors):

1. lines 234-240, 290-292 – The differences in the binding site structures between glycine, AMS, and taurine in Figure 5D, E appear to be incredibly small with respect to the resolution of the structural models. I am skeptical about all of the conclusions that the authors draw in this section about taurine not producing as compact a binding site as glycine or AMS and glycine shifting loops B and C closer to the TMD. Based on the model resolutions, wouldn't it be more apt to conclude that there are no obvious differences in binding site compactness, etc.? If the authors disagree, could you please offer some additional explanation as to why the very small differences (which the authors refer to as "subtle") should be considered sufficiently well resolved to be meaningful? Perhaps densities can be used to make an argument here? Also, the same question for Figure 6 ECD-TMD interface, regarding subtle differences in compactness of this interface with taurine vs glycine or AMS bound.

We appreciate the comments from this reviewer. We agree that the conformation of the binding sites showing in Figure 5E is similar for glycine, AMS, and taurine. Considering the conformational changes of the binding pockets involves a collection of multiple residues, the binding pocket volume can thus more clearly reflect differences in conformations between the different agonists. The volumes of the glycine, AMS and taurine binding pockets are 130, 125 and 151 Å3 respectively. These measurements show that the taurine binding pockets are larger than glycine and AMS binding pockets, consistent with the notion that taurine is not producing as compact a binding pocket as glycine and AMS. The text in Lines 239 and following is revised to read “The volume of glycine, AMS and taurine binding pockets are 130, 125 and 151 Å3 respectively, showing that the binding pocket, when bound with AMS or glycine, takes up a conformation that is more compact than that seen with the partial agonist taurine”.

For the ECD-TMD interface, we agree that the measurements for ECD-TMD interface are similar. We revised the text in Lines 267 and following to read “We found that the ECD-TMD interface are overall similar between glycine, AMS and taurine (Figure 6A-B), as indicated by the distances between the centers of mass of the secondary structure elements.”

2. lines 177, 237, 251 – related to above, please define what "subtle" means.

We agreed with the reviewer’s comments above. Related with Line 177 (old line numbering), we have revised the text in Line 177 and following to read “Importantly, these reconstructions have well-resolved extracellular domain (ECD) and transmembrane domain (TMD) densities, allowing us to observe conformational differences (Figure3—figure supplement 3, Extended-Table 1) in the structures.”

Related with Lines 237 and 251, please see our replies above.

3. The relative lack of a desensitized conformation in the SMA-extracted glycine receptor particles as compared to what would be expected based on functional data should be discussed. Part of this discussion could simply reference the comparison between nanodiscs and SMA in Yu et al., 2021, but it should be at least briefly discussed here as it is highly relevant to the interpretation of the structures.

We appreciate these comments. The related discussion was added with the reference to Yu et al., 2021 cited in Lines 304 and following to read “Indeed, our data shows that, for the AMS-bound receptor, 94.3 % and 3.4% of the particles are in open and desensitized state classes, respectively (Extended-Table 1), consistent with the notion that the particle fractions for desensitized states are much smaller than open states when the receptor is isolated via SMA compared to when it is incorporated into nanodiscs (Yu et al. 2021).”

4. In Figure 3—figure supplement 2, it is clear that the open, desensitized, and expanded-open clusters were selected based on their similarity to previously identified structures. However, the desensitized and expanded-open clusters account for only a couple percent of the total particles, whereas other clusters that were discarded account for larger fractions of the particles. Could the authors please provide a more detailed discussion of what criteria were used to discard these other clusters?

We appreciate these comments. The criteria by which we discarded the ‘bad’ 3D classes generated in Relion were based on TMD features. As shown in Author response image 1, the classes with weak and disconnected TMD were discarded. More details about the 3D classification are added in the Image processing part of the methods section to read “After the extraction of particles, two rounds of 3D classification were performed by cryoSparc to remove the junk particles. One round of heterogeneous refinement with 6 classes was performed. Two classes with GlyR features containing 449,976 particles were selected. A second round of the heterogeneous refinement with 4 classes was performed and one class with good TMD features containing 283,117 particles was chosen. This was followed by a round of non-uniform refinement performed in cryoSparc. These good particles were then exported to Relion 3.1 (Zivanov et al. 2018) for one round of 3D classification. During the 3D classification in Relion 3.1, the particle alignment was closed and the data set was separated into 8 classes with the T value of 20. The 3D classes with good TMD are selected for the subsequent 3D reconstruction.”

Author response image 1

5. line 342 – extra "that".

Done.

Reviewer #2 (Recommendations for the authors):

1. The authors write (lines 60-62) that the binding of high efficacy agonists like glycine leads to channels adopting either open or desensitized states, while binding of partial agonists results in an additional third (intermediate) state that represents agonist-bound closed conformations of the channel. This isn't really true, however, or is an oversimplification that could be confusing to the reader.

The wording of those lines was imprecise, mixing structure and function. This has now been rewritten to be more accurate (lines 51 and following)

If a maximally-effective concentration of glycine has a Po of 0.96, there clearly are brief transitions to glycine-bound closed states of the channel. Wouldn't it be more accurate to state that as agonist efficacy increases, the duration of ligand-bound closed states decreases? Also, what about when low (non-saturating) concentrations of glycine are used? Would one still expect the two states the authors mention?

When the receptor is saturated by the agonist, the proportion of time spent in closed ligand-bound states decreases as the efficacy of the agonist increases.

We do not have structural data at low, non- saturating concentrations of glycine, but we have done the calculations with the best functional model we have for glycine vs. taurine (Lape et al., 2008). Author response table 1 shows the difference in equilibrium occupancy of the states we would see in clusters for three conditions – saturating glycine, saturating taurine and low glycine concentration (chosen to produce the same Popen – 56% -as saturating taurine).

Author response table 1
EQUILIBRIUMOCCUPANCY
0.08mM gly100 mM taurine10 mM gly
Open (=Popen)0.560.560.95
bound closed0.180.440.05
unbound closed0.260.000.00

The calculations show that at low glycine, more than 50% of the shut time is due to the unliganded state (which is effectively absent in the presence of high agonist concentrations), but there is substantial occupancy of the bound closed states, although this is lower than the level predicted for high taurine. It is therefore possible that the additional shut state seen with partial agonists would be structurally detectable at low glycine, but the relation between structural and functional data is only semi quantitative, and we’d rather keep these considerations for further work, as they are too speculative for this paper.

2. In Figure 4D, the AMS_Open and Taurine_Open lines look more similar to one another (except when the distance along the pore axis >30A) than they do with the Glycine_Open line. Also, the taurine and AMS open lines both look markedly different from the glycine line between 10 and 18 angstroms along the pore axis. What is the significance of this?

We agree with the reviewer that AMS_Open and Taurine_Open lines are similar to each other, underscoring the notion that nearly full and partial agonists result in opening of the ion channel to similar open conformations.

We also agree that the Glycine_Open line looks different between 10 and 18 Å. The differences occur at the 9’L (L277), which functions as the constriction point in the Apo and partial agonist bound closed states (Yu et al., 2021, Cell). Upon activation, the side chain of 9’L rotates and the channel opens. While the density of the 9’L becomes weaker at the end of the side chain, the density for the Cα and Cβ are strong and, when we lower the contour level, we can visualize the entire side chain. As a result, we believe that the ion channel radii, as defined by the position of the 9’L side chain, are different. In Author response image 2, we show models and maps, where the maps are all contoured at 4.9σ. While the overall open state conformations of the ion channel are similar between AMS, taurine and glycine, in the glycine bound state there are subtle differences in the conformation of the open state, most notably at 9’L. At present, however, we do not fully understand the molecular mechanism underpinning this difference and await further studies, such as long timescale molecular dynamics simulations.

Author response image 2

3. The single channel data obtained using 100 mM taurine assumes that this concentration is truly maximally-effective. Is this the case? If it is not, could that explain the statement on line 336 that "…taurine is less efficacious than we would predict."?

The single channel data were obtained using 500 mM taurine, not 100 mM. In the whole-cell dose-response experiments shown in Figure 1 , even 300 mM taurine appears to be maximally-effective. We chose 500 mM for the single channel recording to err on the side of caution- as experimental time was limited. Given that these agonists do not block the channel (contrast with nicotinic acetylcholine receptors), using a supramaximal concentration was not a concern.

4. Line 320. How do max Po values of 0.96 and 0.54 yield Eeff values of 60 and 3.8 from the equation Po = Eeff/(1 + Eeff)? Shouldn't they be closer to 24 and 1.2? The same question applies to AMS and taurine on line 321.

In the original manuscript, the Eeff values were calculated from the Popen values as follows: each cluster (longer than 100 ms and with no double openings) gave one Popen and one Eeff value. In the text we then quoted the mean Eeff values for the different agonists.

However, the relation of Eeff to Popen is non-linear, and neither the Popen nor the Eeff values are normally distributed, and this causes the discrepancy spotted by the referee. In our original analysis we had taken into account by reporting both mean and median values of Popen (Table 2) and by using non-parametric statistical tests. However, we had cited mean Eeff values in the text and in the calculations. We have revised this to use the median Popen to calculate Eeff and the energy values in the cycle– the conclusions of the paper are not changed.

5. Do the authors have any thoughts on why the Po, and thus efficacy, of β-alanine and especially taurine went up relative to glycine as the pH was raised from 5 to 7.4 (Table 2)?

Much of this difference is expected because of the non-linear relation of Popen to Eeff, eg. Popen = Eeff/(Eeff+1) and the differences in the initial values of efficacy for the three agonists.

Author response table 2 shows what we expect to happen if we have a general enhancement of gating and an identical increase in efficacy for all our three agonists.

Author response table 2
pH 5Effect of gating enhancementPopen5x Eeff
Median PopenEeff
Glycine0.97640.70.995203.5
Β-alanine0.5651.30.8676.5
Taurine0.0570.060.2310.3

The first two columns show our measurements of maximum Popen (bold) and the estimates of efficacy we obtained at pH 5, the second pair of columns shows the effect of increasing efficacy equally for all agonists by five-fold at pH 7.4.

The increase in Popen for glycine is so small (from 0.976 to 0.995) that it would not be detectable, but the Popen increases for both of the less efficacious agonists, β-alanine and taurine, are large and quite obvious.

As the reviewer noted, at pH 7.4 the Popen of taurine may be higher than what we would expect if the efficacy of all agonists increased by the same amount. It is hard to be sure that this is a real difference given the large scatter of the data for this agonist. The relevant data at pH 7.4 are shown in Figure 4 4C in Ivica et al., 2021 (doi: 10.1074/jbc.RA119.012358)

https://doi.org/10.7554/eLife.79148.sa2

Article and author information

Author details

  1. Josip Ivica

    Department of Neuroscience, Physiology and Pharmacology, Division of Biosciences, University College London, London, United Kingdom
    Present address
    Laboratory for Molecular Biology, Cambridge, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Hongtao Zhu
    Competing interests
    No competing interests declared
  2. Hongtao Zhu

    1. Vollum Institute, Oregon Health and Science University, Portland, United States
    2. Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Josip Ivica
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1522-0500
  3. Remigijus Lape

    Department of Neuroscience, Physiology and Pharmacology, Division of Biosciences, University College London, London, United Kingdom
    Present address
    Laboratory for Molecular Biology, Cambridge, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Eric Gouaux

    1. Vollum Institute, Oregon Health and Science University, Portland, United States
    2. Howard Hughes Medical Institute, Oregon Health & Science University, Portland, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    gouauxe@ohsu.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8549-2360
  5. Lucia G Sivilotti

    Department of Neuroscience, Physiology and Pharmacology, Division of Biosciences, University College London, London, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    l.sivilotti@ucl.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2510-8424

Funding

Medical Research Council (Project grant MR/R009074/1)

  • Lucia G Sivilotti

National Institutes of Health (R01 GM100400)

  • Eric Gouaux

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors would like to thank George Papageorgiou (The Francis Crick Institute, London, United Kingdom) for discussions on the stability of AMS.

Senior Editor

  1. Kenton J Swartz, National Institute of Neurological Disorders and Stroke, National Institutes of Health, United States

Reviewing Editor

  1. Marcel P Goldschen-Ohm, University of Texas at Austin, United States

Reviewer

  1. Marcel P Goldschen-Ohm, University of Texas at Austin, United States

Publication history

  1. Received: March 31, 2022
  2. Preprint posted: May 4, 2022 (view preprint)
  3. Accepted: August 16, 2022
  4. Accepted Manuscript published: August 17, 2022 (version 1)
  5. Version of Record published: September 9, 2022 (version 2)

Copyright

© 2022, Ivica, Zhu et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 271
    Page views
  • 141
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Josip Ivica
  2. Hongtao Zhu
  3. Remigijus Lape
  4. Eric Gouaux
  5. Lucia G Sivilotti
(2022)
Aminomethanesulfonic acid illuminates the boundary between full and partial agonists of the pentameric glycine receptor
eLife 11:e79148.
https://doi.org/10.7554/eLife.79148

Further reading

    1. Neuroscience
    2. Structural Biology and Molecular Biophysics
    Tianzhi Li, Qiqi Cheng ... Cong Ma
    Research Article

    Exocytosis of secretory vesicles requires the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins and small GTPase Rabs. As a Rab3/Rab27 effector protein on secretory vesicles, Rabphilin 3A was implicated to interact with SNAP-25 to regulate vesicle exocytosis in neurons and neuroendocrine cells, yet the underlying mechanism remains unclear. In this study, we have characterized the physiologically relevant binding sites between Rabphilin 3A and SNAP-25. We found that an intramolecular interplay between the N-terminal Rab-binding domain and C-terminal C2AB domain enables Rabphilin 3A to strongly bind the SNAP-25 N-peptide region via its C2B bottom α-helix. Disruption of this interaction significantly impaired docking and fusion of vesicles with the plasma membrane in rat PC12 cells. In addition, we found that this interaction allows Rabphilin 3A to accelerate SNARE complex assembly. Furthermore, we revealed that this interaction accelerates SNARE complex assembly via inducing a conformational switch from random coils to α-helical structure in the SNAP-25 SNARE motif. Altogether, our data suggest that the promotion of SNARE complex assembly by binding the C2B bottom α-helix of Rabphilin 3A to the N-peptide of SNAP-25 underlies a pre-fusion function of Rabphilin 3A in vesicle exocytosis.

    1. Chromosomes and Gene Expression
    2. Structural Biology and Molecular Biophysics
    Lena Maria Muckenfuss, Anabel Carmen Migenda Herranz ... Martin Jinek
    Research Article Updated

    3′ end formation of most eukaryotic mRNAs is dependent on the assembly of a ~1.5 MDa multiprotein complex, that catalyzes the coupled reaction of pre-mRNA cleavage and polyadenylation. In mammals, the cleavage and polyadenylation specificity factor (CPSF) constitutes the core of the 3′ end processing machinery onto which the remaining factors, including cleavage stimulation factor (CstF) and poly(A) polymerase (PAP), assemble. These interactions are mediated by Fip1, a CPSF subunit characterized by high degree of intrinsic disorder. Here, we report two crystal structures revealing the interactions of human Fip1 (hFip1) with CPSF30 and CstF77. We demonstrate that CPSF contains two copies of hFip1, each binding to the zinc finger (ZF) domains 4 and 5 of CPSF30. Using polyadenylation assays we show that the two hFip1 copies are functionally redundant in recruiting one copy of PAP, thereby increasing the processivity of RNA polyadenylation. We further show that the interaction between hFip1 and CstF77 is mediated via a short motif in the N-terminal ‘acidic’ region of hFip1. In turn, CstF77 competitively inhibits CPSF-dependent PAP recruitment and 3′ polyadenylation. Taken together, these results provide a structural basis for the multivalent scaffolding and regulatory functions of hFip1 in 3′ end processing.